As presented in DETR [1]}, transformer architecture is permutation-invariant, which cannot leverage the order of the tokens in an input sequence. To mitigate this gap, previous works [2]}, [3]} add an absolute positional encoding to each token in the input sequence, which enables order-awareness. However, in co-saliency detection task, we want the model should be insensitive to input order when capturing group-wise relationships. Hence, it is natural to use transformer to model group-wise relationships without positional encoding. To our knowledge, thus far there are no prior applications of Transformers to co-saliency detection.
<FIGURE>